




"""simple docstring"""

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import YolosImageProcessor
class       lowerCamelCase__     (	unittest.TestCase      ):


			def __init__(  self					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE=7					,			SCREAMING_SNAKE_CASE=3					,			SCREAMING_SNAKE_CASE=30					,			SCREAMING_SNAKE_CASE=400					,			SCREAMING_SNAKE_CASE=True					,			SCREAMING_SNAKE_CASE=None					,			SCREAMING_SNAKE_CASE=True					,			SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]					,			SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]					,			SCREAMING_SNAKE_CASE=True					,			SCREAMING_SNAKE_CASE=1 / 255					,			SCREAMING_SNAKE_CASE=True					,			):



					"""simple docstring"""







					snake_case		:    str             =					size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
					snake_case		:    List[Any]             =					parent
					snake_case		:    Dict             =					batch_size
					snake_case		:    str             =					num_channels
					snake_case		:    List[str]             =					min_resolution
					snake_case		:    str             =					max_resolution
					snake_case		:    List[Any]             =					do_resize
					snake_case		:    Optional[Any]             =					size
					snake_case		:    List[Any]             =					do_normalize
					snake_case		:    List[Any]             =					image_mean
					snake_case		:    Tuple             =					image_std
					snake_case		:    List[Any]             =					do_rescale
					snake_case		:    Tuple             =					rescale_factor
					snake_case		:    List[str]             =					do_pad


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}



			def 				lowerCamelCase_					(  self					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE=False				):



					"""simple docstring"""







					if not batched:
							snake_case		:    Union[str, Any]             =					image_inputs[0]
							if isinstance(lowerCamelCase_					,			Image.Image				):
									snake_case      ,					snake_case		:    Optional[int]             =					image.size
							else:
									snake_case      ,					snake_case		:    Union[str, Any]             =					image.shape[1], image.shape[2]
							if w < h:
									snake_case		:    Optional[Any]             =					int(self.size["shortest_edge"] * h / w				)
									snake_case		:    Tuple             =					self.size["shortest_edge"]
							elif w > h:
									snake_case		:    Union[str, Any]             =					self.size["shortest_edge"]
									snake_case		:    int             =					int(self.size["shortest_edge"] * w / h				)
							else:
									snake_case		:    str             =					self.size["shortest_edge"]
									snake_case		:    Any             =					self.size["shortest_edge"]

					else:
							snake_case		:    Union[str, Any]             =					[]
							for image in image_inputs:
									snake_case      ,					snake_case		:    Dict             =					self.get_expected_values([image]				)
									expected_values.append((expected_height, expected_width)				)
							snake_case		:    Optional[Any]             =					max(lowerCamelCase_					,			key=lambda SCREAMING_SNAKE_CASE				: item[0]				)[0]
							snake_case		:    str             =					max(lowerCamelCase_					,			key=lambda SCREAMING_SNAKE_CASE				: item[1]				)[1]

					return expected_height, expected_width







@require_torch
@require_vision
class       lowerCamelCase__     (	__lowerCAmelCase					,	unittest.TestCase      ):
			a__			:   int             =  YolosImageProcessor if is_vision_available() else None


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    str             =					YolosImageProcessingTester(self				)


			@property
			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Union[str, Any]             =					self.image_processing_class(**self.image_processor_dict				)
					self.assertTrue(hasattr(lowerCamelCase_					,			"image_mean"				)				)
					self.assertTrue(hasattr(lowerCamelCase_					,			"image_std"				)				)
					self.assertTrue(hasattr(lowerCamelCase_					,			"do_normalize"				)				)
					self.assertTrue(hasattr(lowerCamelCase_					,			"do_resize"				)				)
					self.assertTrue(hasattr(lowerCamelCase_					,			"size"				)				)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Any             =					self.image_processing_class.from_dict(self.image_processor_dict				)
					self.assertEqual(image_processor.size					,			{"shortest_edge": 18, "longest_edge": 1_333}				)
					self.assertEqual(image_processor.do_pad					,			lowerCamelCase_				)

					snake_case		:    Any             =					self.image_processing_class.from_dict(
					    self.image_processor_dict					,			size=42					,			max_size=84					,			pad_and_return_pixel_mask=lowerCamelCase_				)
					self.assertEqual(image_processor.size					,			{"shortest_edge": 42, "longest_edge": 84}				)
					self.assertEqual(image_processor.do_pad					,			lowerCamelCase_				)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					pass


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    List[str]             =					self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					snake_case		:    Tuple             =					prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase_				)
					for image in image_inputs:
							self.assertIsInstance(lowerCamelCase_					,			Image.Image				)

					# Test not batched input
					snake_case		:    int             =					image_processing(image_inputs[0]					,			return_tensors="pt"				).pixel_values

					snake_case      ,					snake_case		:    Optional[Any]             =					self.image_processor_tester.get_expected_values(lowerCamelCase_				)

					self.assertEqual(
					    encoded_images.shape					,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,			)

					# Test batched
					snake_case      ,					snake_case		:    Union[str, Any]             =					self.image_processor_tester.get_expected_values(lowerCamelCase_					,			batched=lowerCamelCase_				)

					snake_case		:    List[str]             =					image_processing(lowerCamelCase_					,			return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape					,			(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,			)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    str             =					self.image_processing_class(**self.image_processor_dict				)
					# create random numpy tensors
					snake_case		:    int             =					prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase_					,			numpify=lowerCamelCase_				)
					for image in image_inputs:
							self.assertIsInstance(lowerCamelCase_					,			np.ndarray				)

					# Test not batched input
					snake_case		:    Optional[Any]             =					image_processing(image_inputs[0]					,			return_tensors="pt"				).pixel_values

					snake_case      ,					snake_case		:    Any             =					self.image_processor_tester.get_expected_values(lowerCamelCase_				)

					self.assertEqual(
					    encoded_images.shape					,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,			)

					# Test batched
					snake_case		:    int             =					image_processing(lowerCamelCase_					,			return_tensors="pt"				).pixel_values

					snake_case      ,					snake_case		:    Optional[int]             =					self.image_processor_tester.get_expected_values(lowerCamelCase_					,			batched=lowerCamelCase_				)

					self.assertEqual(
					    encoded_images.shape					,			(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,			)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Dict             =					self.image_processing_class(**self.image_processor_dict				)
					# create random PyTorch tensors
					snake_case		:    List[Any]             =					prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase_					,			torchify=lowerCamelCase_				)
					for image in image_inputs:
							self.assertIsInstance(lowerCamelCase_					,			torch.Tensor				)

					# Test not batched input
					snake_case		:    Any             =					image_processing(image_inputs[0]					,			return_tensors="pt"				).pixel_values

					snake_case      ,					snake_case		:    str             =					self.image_processor_tester.get_expected_values(lowerCamelCase_				)

					self.assertEqual(
					    encoded_images.shape					,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,			)

					# Test batched
					snake_case		:    int             =					image_processing(lowerCamelCase_					,			return_tensors="pt"				).pixel_values

					snake_case      ,					snake_case		:    str             =					self.image_processor_tester.get_expected_values(lowerCamelCase_					,			batched=lowerCamelCase_				)

					self.assertEqual(
					    encoded_images.shape					,			(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,			)


			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    List[str]             =					self.image_processing_class(**self.image_processor_dict				)
					snake_case		:    str             =					self.image_processing_class(do_resize=lowerCamelCase_					,			do_normalize=lowerCamelCase_					,			do_rescale=lowerCamelCase_				)
					# create random PyTorch tensors
					snake_case		:    List[str]             =					prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase_					,			torchify=lowerCamelCase_				)
					for image in image_inputs:
							self.assertIsInstance(lowerCamelCase_					,			torch.Tensor				)

					# Test whether the method "pad" and calling the image processor return the same tensors
					snake_case		:    Dict             =					image_processing_a.pad(lowerCamelCase_					,			return_tensors="pt"				)
					snake_case		:    List[Any]             =					image_processing_a(lowerCamelCase_					,			return_tensors="pt"				)

					self.assertTrue(
					    torch.allclose(encoded_images_with_method["pixel_values"]					,			encoded_images["pixel_values"]					,			atol=1E-4				)				)


			@slow
			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Optional[int]             =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,			"r"				) as f:
							snake_case		:    int             =					json.loads(f.read()				)

					snake_case		:    Optional[int]             =					{"image_id": 39_769, "annotations": target}

					# encode them
					snake_case		:    Dict             =					YolosImageProcessor.from_pretrained("hustvl/yolos-small"				)
					snake_case		:    Union[str, Any]             =					image_processing(images=lowerCamelCase_					,			annotations=lowerCamelCase_					,			return_tensors="pt"				)

					# verify pixel values
					snake_case		:    List[Any]             =					torch.Size([1, 3, 800, 1_066]				)
					self.assertEqual(encoding["pixel_values"].shape					,			lowerCamelCase_				)

					snake_case		:    List[Any]             =					torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,			lowerCamelCase_					,			atol=1E-4				)				)

					# verify area
					snake_case		:    Tuple             =					torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,			lowerCamelCase_				)				)
					# verify boxes
					snake_case		:    List[str]             =					torch.Size([6, 4]				)
					self.assertEqual(encoding["labels"][0]["boxes"].shape					,			lowerCamelCase_				)
					snake_case		:    Dict             =					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,			lowerCamelCase_					,			atol=1E-3				)				)
					# verify image_id
					snake_case		:    Union[str, Any]             =					torch.tensor([39_769]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,			lowerCamelCase_				)				)
					# verify is_crowd
					snake_case		:    Union[str, Any]             =					torch.tensor([0, 0, 0, 0, 0, 0]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,			lowerCamelCase_				)				)
					# verify class_labels
					snake_case		:    Union[str, Any]             =					torch.tensor([75, 75, 63, 65, 17, 17]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,			lowerCamelCase_				)				)
					# verify orig_size
					snake_case		:    Tuple             =					torch.tensor([480, 640]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,			lowerCamelCase_				)				)
					# verify size
					snake_case		:    str             =					torch.tensor([800, 1_066]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,			lowerCamelCase_				)				)



			@slow
			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					snake_case		:    Optional[int]             =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,			"r"				) as f:
							snake_case		:    Union[str, Any]             =					json.loads(f.read()				)

					snake_case		:    List[str]             =					{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

					snake_case		:    List[Any]             =					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"				)

					# encode them
					snake_case		:    str             =					YolosImageProcessor(format="coco_panoptic"				)
					snake_case		:    Dict             =					image_processing(images=lowerCamelCase_					,			annotations=lowerCamelCase_					,			masks_path=lowerCamelCase_					,			return_tensors="pt"				)

					# verify pixel values
					snake_case		:    Optional[Any]             =					torch.Size([1, 3, 800, 1_066]				)
					self.assertEqual(encoding["pixel_values"].shape					,			lowerCamelCase_				)

					snake_case		:    Union[str, Any]             =					torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,			lowerCamelCase_					,			atol=1E-4				)				)

					# verify area
					snake_case		:    Dict             =					torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,			lowerCamelCase_				)				)
					# verify boxes
					snake_case		:    Union[str, Any]             =					torch.Size([6, 4]				)
					self.assertEqual(encoding["labels"][0]["boxes"].shape					,			lowerCamelCase_				)
					snake_case		:    Union[str, Any]             =					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,			lowerCamelCase_					,			atol=1E-3				)				)
					# verify image_id
					snake_case		:    Any             =					torch.tensor([39_769]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,			lowerCamelCase_				)				)
					# verify is_crowd
					snake_case		:    Union[str, Any]             =					torch.tensor([0, 0, 0, 0, 0, 0]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,			lowerCamelCase_				)				)
					# verify class_labels
					snake_case		:    Dict             =					torch.tensor([17, 17, 63, 75, 75, 93]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,			lowerCamelCase_				)				)
					# verify masks
					snake_case		:    Tuple             =					822_873
					self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,			lowerCamelCase_				)
					# verify orig_size
					snake_case		:    List[Any]             =					torch.tensor([480, 640]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,			lowerCamelCase_				)				)
					# verify size
					snake_case		:    Tuple             =					torch.tensor([800, 1_066]				)
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,			lowerCamelCase_				)				)


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int = 128   ,   lowerCamelCase_						:						int = 256   ,   lowerCamelCase_						:						float = 2_0_0_0.0   ,   lowerCamelCase_						:						int = 768   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 64   ,   lowerCamelCase_						:						int = 2048   ,   lowerCamelCase_						:						float = 0.1   ,   ):





    """simple docstring"""


    super().__init__()

    UpperCamelCase			  =      nn.Sequential(
        nn.Linear(lowerCamelCase_   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   )

    UpperCamelCase			  =      nn.Embedding(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      False

    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)

    UpperCamelCase			  =      nn.ModuleList()
    for lyr_num in range(lowerCamelCase_				):
     # FiLM conditional T5 decoder
     UpperCamelCase			  =      DecoderLayer(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
     self.decoders.append(lowerCamelCase_				)

    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.mul(query_input.unsqueeze(-1				)   ,   key_input.unsqueeze(-2				)				)
    return mask.unsqueeze(-3				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCamelCase			  =      get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype				)

    UpperCamelCase			  =      self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCamelCase			  =      decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCamelCase			  =      torch.broadcast_to(
        torch.arange(lowerCamelCase_   ,   device=decoder_input_tokens.device				)   ,   (batch, seq_length)   ,   )

    UpperCamelCase			  =      self.position_encoding(lowerCamelCase_				)

    UpperCamelCase			  =      self.continuous_inputs_projection(lowerCamelCase_				)
    inputs += position_encodings
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    # decoder: No padding present.
    UpperCamelCase			  =      torch.ones(
        decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype				)

    # Translate encoding masks to encoder-decoder masks.
    UpperCamelCase			  =      [(x, self.encoder_decoder_mask(lowerCamelCase_   ,   lowerCamelCase_				)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCamelCase			  =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1				)
    UpperCamelCase			  =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1				)

    for lyr in self.decoders:
     UpperCamelCase			  =      lyr(
         lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      self.decoder_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.post_dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.spec_out(lowerCamelCase_				)
    return spec_out





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Any=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)				)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_   ,   )				)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer[0](
        lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    if encoder_hidden_states is not None:
     UpperCamelCase			  =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1E10				).to(
         encoder_hidden_states.dtype				)

     UpperCamelCase			  =      self.layer[1](
         lowerCamelCase_   ,   key_value_states=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    # Apply Film Conditional Feed Forward layer
    UpperCamelCase			  =      self.layer[-1](lowerCamelCase_   ,   lowerCamelCase_				)

    return (hidden_states,)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)

    if conditioning_emb is not None:
     UpperCamelCase			  =      self.FiLMLayer(lowerCamelCase_   ,   lowerCamelCase_				)

    # Self-attention block
    UpperCamelCase			  =      self.attention(lowerCamelCase_				)

    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)

    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.attention(
        lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   attention_mask=attention_mask.squeeze(1				)   ,   )
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return layer_output





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaDenseGatedActDense(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    if conditioning_emb is not None:
     UpperCamelCase			  =      self.film(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.DenseReluDense(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)
    UpperCamelCase			  =      NewGELUActivation()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.act(self.wi_a(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.wi_a(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_gelu * hidden_linear
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.wo(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Parameter(torch.ones(lowerCamelCase_				)				)
    UpperCamelCase			  =      eps







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_states.to(torch.floataa				).pow(2				).mean(-1   ,   keepdim=lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCamelCase			  =      hidden_states.to(self.weight.dtype				)

    return self.weight * hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase_   ,   3.0				))				))





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   out_features * 2   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scale_bias(lowerCamelCase_				)
    UpperCamelCase  ,     UpperCamelCase			  =      torch.chunk(lowerCamelCase_   ,   2   ,   -1				)
    UpperCamelCase			  =      x * (1 + scale) + shift
    return x
